Overview

Dataset statistics

Number of variables16
Number of observations215980
Missing cells370175
Missing cells (%)10.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.5 MiB
Average record size in memory419.8 B

Variable types

Categorical5
Numeric10
Boolean1

Alerts

src has a high cardinality: 66111 distinct values High cardinality
dst has a high cardinality: 66111 distinct values High cardinality
date has a high cardinality: 79 distinct values High cardinality
mes_sent is highly correlated with mes_received and 1 other fieldsHigh correlation
mes_received is highly correlated with mes_sent and 1 other fieldsHigh correlation
contr_index is highly correlated with mes_sent and 1 other fieldsHigh correlation
neighbours is highly correlated with neighbours_clusterHigh correlation
neighbours_cluster is highly correlated with neighbours and 2 other fieldsHigh correlation
neighbours_!cluster is highly correlated with neighbours_cluster and 1 other fieldsHigh correlation
cluster_equal is highly correlated with neighbours_cluster and 1 other fieldsHigh correlation
mes_sent is highly correlated with mes_totalHigh correlation
mes_received is highly correlated with mes_totalHigh correlation
mes_total is highly correlated with mes_sent and 1 other fieldsHigh correlation
neighbours is highly correlated with neighbours_clusterHigh correlation
neighbours_cluster is highly correlated with neighboursHigh correlation
mes_sent is highly correlated with mes_received and 1 other fieldsHigh correlation
mes_received is highly correlated with mes_sent and 1 other fieldsHigh correlation
contr_index is highly correlated with mes_sent and 1 other fieldsHigh correlation
neighbours is highly correlated with neighbours_clusterHigh correlation
neighbours_cluster is highly correlated with neighboursHigh correlation
neighbours_!cluster is highly correlated with cluster_equalHigh correlation
cluster_equal is highly correlated with neighbours_!clusterHigh correlation
cluster_src is highly correlated with cluster_dstHigh correlation
cluster_dst is highly correlated with cluster_srcHigh correlation
cluster_src is highly correlated with cluster_dstHigh correlation
mes_sent is highly correlated with mes_received and 1 other fieldsHigh correlation
mes_received is highly correlated with mes_sent and 1 other fieldsHigh correlation
mes_total is highly correlated with mes_sent and 1 other fieldsHigh correlation
contr_index is highly correlated with neighbours and 1 other fieldsHigh correlation
sentiment_avg is highly correlated with emoti_avgHigh correlation
emoti_avg is highly correlated with sentiment_avgHigh correlation
neighbours is highly correlated with contr_index and 1 other fieldsHigh correlation
neighbours_cluster is highly correlated with contr_index and 1 other fieldsHigh correlation
cluster_dst is highly correlated with cluster_srcHigh correlation
sentiment_avg has 123390 (57.1%) missing values Missing
emoti_avg has 123395 (57.1%) missing values Missing
compl_avg has 123390 (57.1%) missing values Missing
mes_sent is highly skewed (γ1 = 28.29589058) Skewed
mes_received is highly skewed (γ1 = 28.24366599) Skewed
mes_total is highly skewed (γ1 = 27.63652063) Skewed
neighbours_!cluster is highly skewed (γ1 = 20.35366196) Skewed
mes_sent has 98345 (45.5%) zeros Zeros
mes_received has 97268 (45.0%) zeros Zeros
contr_index has 18628 (8.6%) zeros Zeros
neighbours_cluster has 28054 (13.0%) zeros Zeros
neighbours_!cluster has 187926 (87.0%) zeros Zeros

Reproduction

Analysis started2022-01-20 09:35:29.219204
Analysis finished2022-01-20 09:36:15.209613
Duration45.99 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

src
Categorical

HIGH CARDINALITY

Distinct66111
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size15.7 MiB
[twitter_query=sol]
 
7750
babydogecoin
 
1852
watcherguru
 
1277
mexc_global
 
869
realakamaruinu
 
509
Other values (66106)
203723 

Length

Max length19
Median length11
Mean length11.42435874
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34057 ?
Unique (%)15.8%

Sample

1st row000126373b
2nd row000126373b
3rd row000126373b
4th row000126373b
5th row0001_yousef

Common Values

ValueCountFrequency (%)
[twitter_query=sol]7750
 
3.6%
babydogecoin1852
 
0.9%
watcherguru1277
 
0.6%
mexc_global869
 
0.4%
realakamaruinu509
 
0.2%
essentialesc388
 
0.2%
jack_concours359
 
0.2%
chris91195137351
 
0.2%
robinhoodapp350
 
0.2%
investments_ceo343
 
0.2%
Other values (66101)201932
93.5%

Length

2022-01-20T10:36:15.350245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
twitter_query=sol7750
 
3.6%
babydogecoin1852
 
0.9%
watcherguru1277
 
0.6%
mexc_global869
 
0.4%
realakamaruinu509
 
0.2%
essentialesc388
 
0.2%
jack_concours359
 
0.2%
chris91195137351
 
0.2%
robinhoodapp350
 
0.2%
investments_ceo343
 
0.2%
Other values (66084)201932
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dst
Categorical

HIGH CARDINALITY

Distinct66111
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Memory size15.7 MiB
[twitter_query=sol]
 
7298
babydogecoin
 
1840
watcherguru
 
1239
mexc_global
 
865
realakamaruinu
 
508
Other values (66106)
204230 

Length

Max length19
Median length11
Mean length11.40313455
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37189 ?
Unique (%)17.2%

Sample

1st rowoh73hh
2nd rowoh73hh
3rd rowoh73hh
4th rowoh73hh
5th rowdogelon_muskk

Common Values

ValueCountFrequency (%)
[twitter_query=sol]7298
 
3.4%
babydogecoin1840
 
0.9%
watcherguru1239
 
0.6%
mexc_global865
 
0.4%
realakamaruinu508
 
0.2%
essentialesc388
 
0.2%
jack_concours358
 
0.2%
chris91195137351
 
0.2%
cryptobri_342
 
0.2%
investments_ceo340
 
0.2%
Other values (66101)202451
93.7%

Length

2022-01-20T10:36:15.593711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
twitter_query=sol7298
 
3.4%
babydogecoin1840
 
0.9%
watcherguru1239
 
0.6%
mexc_global865
 
0.4%
realakamaruinu508
 
0.2%
essentialesc388
 
0.2%
jack_concours358
 
0.2%
chris91195137351
 
0.2%
cryptobri342
 
0.2%
investments_ceo340
 
0.2%
Other values (66084)202451
93.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

date
Categorical

HIGH CARDINALITY

Distinct79
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
2021-11-07 00:00:00
32104 
2021-12-05 00:00:00
27639 
2021-10-31 00:00:00
25802 
2021-12-12 00:00:00
24162 
2021-12-26 00:00:00
23298 
Other values (74)
82975 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-12-20 00:00:00
2nd row2021-12-20 00:00:00
3rd row2021-12-20 00:00:00
4th row2021-12-20 00:00:00
5th row2021-12-20 00:00:00

Common Values

ValueCountFrequency (%)
2021-11-07 00:00:0032104
14.9%
2021-12-05 00:00:0027639
12.8%
2021-10-31 00:00:0025802
11.9%
2021-12-12 00:00:0024162
11.2%
2021-12-26 00:00:0023298
10.8%
2021-11-14 00:00:0020260
9.4%
2021-12-21 00:00:0017866
8.3%
2021-11-29 00:00:0015640
7.2%
2021-11-28 00:00:0013243
6.1%
2021-12-20 00:00:006559
 
3.0%
Other values (69)9407
 
4.4%

Length

2022-01-20T10:36:15.742282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00215980
50.0%
2021-11-0732104
 
7.4%
2021-12-0527639
 
6.4%
2021-10-3125802
 
6.0%
2021-12-1224162
 
5.6%
2021-12-2623298
 
5.4%
2021-11-1420260
 
4.7%
2021-12-2117866
 
4.1%
2021-11-2915640
 
3.6%
2021-11-2813243
 
3.1%
Other values (70)15966
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cluster_src
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
C7
135639 
C2
22189 
C0
19234 
C5
 
9066
C6
 
8490
Other values (5)
21362 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC2
2nd rowC2
3rd rowC7
4th rowC7
5th rowC7

Common Values

ValueCountFrequency (%)
C7135639
62.8%
C222189
 
10.3%
C019234
 
8.9%
C59066
 
4.2%
C68490
 
3.9%
C37881
 
3.6%
C97313
 
3.4%
C84579
 
2.1%
C41570
 
0.7%
C119
 
< 0.1%

Length

2022-01-20T10:36:15.890883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-20T10:36:16.001629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
c7135639
62.8%
c222189
 
10.3%
c019234
 
8.9%
c59066
 
4.2%
c68490
 
3.9%
c37881
 
3.6%
c97313
 
3.4%
c84579
 
2.1%
c41570
 
0.7%
c119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

mes_sent
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7370080563
Minimum0
Maximum150
Zeros98345
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:16.186131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum150
Range150
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.783875334
Coefficient of variation (CV)2.420428541
Kurtosis1433.069367
Mean0.7370080563
Median Absolute Deviation (MAD)1
Skewness28.29589058
Sum159179
Variance3.182211209
MonotonicityNot monotonic
2022-01-20T10:36:16.365651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1101565
47.0%
098345
45.5%
210187
 
4.7%
32430
 
1.1%
41115
 
0.5%
5685
 
0.3%
6385
 
0.2%
7254
 
0.1%
8162
 
0.1%
9129
 
0.1%
Other values (61)723
 
0.3%
ValueCountFrequency (%)
098345
45.5%
1101565
47.0%
210187
 
4.7%
32430
 
1.1%
41115
 
0.5%
5685
 
0.3%
6385
 
0.2%
7254
 
0.1%
8162
 
0.1%
9129
 
0.1%
ValueCountFrequency (%)
1501
 
< 0.1%
1311
 
< 0.1%
1292
 
< 0.1%
1104
< 0.1%
1071
 
< 0.1%
1041
 
< 0.1%
922
 
< 0.1%
861
 
< 0.1%
855
< 0.1%
841
 
< 0.1%

mes_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.743175294
Minimum0
Maximum150
Zeros97268
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:16.768571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum150
Range150
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.779770207
Coefficient of variation (CV)2.394818855
Kurtosis1433.57323
Mean0.743175294
Median Absolute Deviation (MAD)1
Skewness28.24366599
Sum160511
Variance3.167581989
MonotonicityNot monotonic
2022-01-20T10:36:16.952081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102440
47.4%
097268
45.0%
210340
 
4.8%
32466
 
1.1%
41118
 
0.5%
5687
 
0.3%
6386
 
0.2%
7257
 
0.1%
8165
 
0.1%
9131
 
0.1%
Other values (61)722
 
0.3%
ValueCountFrequency (%)
097268
45.0%
1102440
47.4%
210340
 
4.8%
32466
 
1.1%
41118
 
0.5%
5687
 
0.3%
6386
 
0.2%
7257
 
0.1%
8165
 
0.1%
9131
 
0.1%
ValueCountFrequency (%)
1501
 
< 0.1%
1311
 
< 0.1%
1292
 
< 0.1%
1104
< 0.1%
1071
 
< 0.1%
1041
 
< 0.1%
921
 
< 0.1%
861
 
< 0.1%
855
< 0.1%
842
 
< 0.1%

mes_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.48018335
Minimum1
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:17.175447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum258
Range257
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.768112989
Coefficient of variation (CV)1.870114934
Kurtosis1382.706425
Mean1.48018335
Median Absolute Deviation (MAD)0
Skewness27.63652063
Sum319690
Variance7.662449519
MonotonicityNot monotonic
2022-01-20T10:36:17.373943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1174843
81.0%
228275
 
13.1%
43883
 
1.8%
33709
 
1.7%
61323
 
0.6%
5935
 
0.4%
8653
 
0.3%
10363
 
0.2%
7349
 
0.2%
12235
 
0.1%
Other values (72)1412
 
0.7%
ValueCountFrequency (%)
1174843
81.0%
228275
 
13.1%
33709
 
1.7%
43883
 
1.8%
5935
 
0.4%
61323
 
0.6%
7349
 
0.2%
8653
 
0.3%
9180
 
0.1%
10363
 
0.2%
ValueCountFrequency (%)
2582
 
< 0.1%
1662
 
< 0.1%
1502
 
< 0.1%
1312
 
< 0.1%
1292
 
< 0.1%
1282
 
< 0.1%
1122
 
< 0.1%
1108
< 0.1%
1072
 
< 0.1%
1045
< 0.1%

contr_index
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct119
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.004997684971
Minimum-1
Maximum1
Zeros18628
Zeros (%)8.6%
Negative99219
Negative (%)45.9%
Memory size3.3 MiB
2022-01-20T10:36:17.614305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median0
Q31
95-th percentile1
Maximum1
Range2
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9524131311
Coefficient of variation (CV)-190.5708616
Kurtosis-1.898631807
Mean-0.004997684971
Median Absolute Deviation (MAD)1
Skewness0.00997317512
Sum-1079.4
Variance0.9070907722
MonotonicityNot monotonic
2022-01-20T10:36:17.822751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-198345
45.5%
197268
45.0%
018628
 
8.6%
-0.33437
 
0.2%
0.33433
 
0.2%
-0.293
 
< 0.1%
0.289
 
< 0.1%
-0.574
 
< 0.1%
0.572
 
< 0.1%
-0.638
 
< 0.1%
Other values (109)503
 
0.2%
ValueCountFrequency (%)
-198345
45.5%
-0.961
 
< 0.1%
-0.923
 
< 0.1%
-0.9112
 
< 0.1%
-0.94
 
< 0.1%
-0.891
 
< 0.1%
-0.881
 
< 0.1%
-0.861
 
< 0.1%
-0.852
 
< 0.1%
-0.823
 
< 0.1%
ValueCountFrequency (%)
197268
45.0%
0.961
 
< 0.1%
0.923
 
< 0.1%
0.9112
 
< 0.1%
0.94
 
< 0.1%
0.891
 
< 0.1%
0.881
 
< 0.1%
0.861
 
< 0.1%
0.852
 
< 0.1%
0.824
 
< 0.1%

sentiment_avg
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct30669
Distinct (%)33.1%
Missing123390
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean0.5890322375
Minimum1.430749279 × 10-7
Maximum0.9999995232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:18.055095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.430749279 × 10-7
5-th percentile0.03755706921
Q10.400000006
median0.6456560493
Q30.8264799714
95-th percentile0.9826896191
Maximum0.9999995232
Range0.9999993801
Interquartile range (IQR)0.4264799654

Descriptive statistics

Standard deviation0.2859846336
Coefficient of variation (CV)0.4855160981
Kurtosis-0.761165259
Mean0.5890322375
Median Absolute Deviation (MAD)0.2089950442
Skewness-0.4541639752
Sum54538.49487
Variance0.08178721067
MonotonicityNot monotonic
2022-01-20T10:36:18.246619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.64565604934110
 
1.9%
0.6553332211461
 
0.7%
0.9826896191440
 
0.2%
0.7685244083421
 
0.2%
0.9638491273410
 
0.2%
0.1132522821356
 
0.2%
0.4786175787354
 
0.2%
0.6103714705328
 
0.2%
0.9706734419312
 
0.1%
0.3964265883297
 
0.1%
Other values (30659)84101
38.9%
(Missing)123390
57.1%
ValueCountFrequency (%)
1.430749279 × 10-74
 
< 0.1%
3.1885412 × 10-72
 
< 0.1%
3.918133302 × 10-71
 
< 0.1%
5.638288485 × 10-710
< 0.1%
1.165124104 × 10-63
 
< 0.1%
1.504749321 × 10-63
 
< 0.1%
1.659620125 × 10-61
 
< 0.1%
1.824472406 × 10-61
 
< 0.1%
2.838787623 × 10-62
 
< 0.1%
2.854755621 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.99999952321
 
< 0.1%
0.99999904633
< 0.1%
0.9999977351
 
< 0.1%
0.99999767543
< 0.1%
0.9999968411
 
< 0.1%
0.99999529122
< 0.1%
0.99999386073
< 0.1%
0.99999254941
 
< 0.1%
0.9999923114
< 0.1%
0.99998921161
 
< 0.1%

emoti_avg
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct30608
Distinct (%)33.1%
Missing123395
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean0.2324705019
Minimum0.000616976351
Maximum0.979193747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:18.457054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.000616976351
5-th percentile0.1068886831
Q10.1549674571
median0.2203656286
Q30.2917417288
95-th percentile0.402022779
Maximum0.979193747
Range0.9785767707
Interquartile range (IQR)0.1367742717

Descriptive statistics

Standard deviation0.09694122046
Coefficient of variation (CV)0.4170043927
Kurtosis2.635938457
Mean0.2324705019
Median Absolute Deviation (MAD)0.06813779473
Skewness0.9465887561
Sum21523.28142
Variance0.009397600224
MonotonicityNot monotonic
2022-01-20T10:36:18.650542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14889603854597
 
2.1%
0.15222783391461
 
0.7%
0.3301796019440
 
0.2%
0.3283373415410
 
0.2%
0.1867872924356
 
0.2%
0.3143947721354
 
0.2%
0.1291356981328
 
0.2%
0.3199320734312
 
0.1%
0.2071468383297
 
0.1%
0.200000003283
 
0.1%
Other values (30598)83747
38.8%
(Missing)123395
57.1%
ValueCountFrequency (%)
0.00061697635130
< 0.1%
0.0018729780571
 
< 0.1%
0.0023277807052
 
< 0.1%
0.0023483906413
 
< 0.1%
0.0030211480335
 
< 0.1%
0.0047598443932
 
< 0.1%
0.0048294332813
 
< 0.1%
0.0048697120512
 
< 0.1%
0.0049965213982
 
< 0.1%
0.00513918185615
< 0.1%
ValueCountFrequency (%)
0.9791937471
 
< 0.1%
0.979071259525
< 0.1%
0.97525238994
 
< 0.1%
0.95987260341
 
< 0.1%
0.93782961371
 
< 0.1%
0.93554502734
 
< 0.1%
0.92235362531
 
< 0.1%
0.92192190891
 
< 0.1%
0.91354292631
 
< 0.1%
0.89564323431
 
< 0.1%

compl_avg
Real number (ℝ≥0)

MISSING

Distinct30563
Distinct (%)33.0%
Missing123390
Missing (%)57.1%
Infinite0
Infinite (%)0.0%
Mean6.505543696
Minimum1.887231827
Maximum13.6331892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:18.937735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.887231827
5-th percentile4.136986995
Q15.239589095
median6.091587067
Q37.426179409
95-th percentile10.17917061
Maximum13.6331892
Range11.74595737
Interquartile range (IQR)2.186590314

Descriptive statistics

Standard deviation1.915745596
Coefficient of variation (CV)0.2944789376
Kurtosis1.077219802
Mean6.505543696
Median Absolute Deviation (MAD)1.00048542
Skewness1.033885605
Sum602348.2908
Variance3.67008119
MonotonicityNot monotonic
2022-01-20T10:36:19.170800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.2645506864590
 
2.1%
5.8082919121461
 
0.7%
4.947360516440
 
0.2%
4.938356876410
 
0.2%
7.279335976356
 
0.2%
9.875304222354
 
0.2%
7.819937229328
 
0.2%
5.692192078312
 
0.1%
10.0138607293
 
0.1%
12.31143284281
 
0.1%
Other values (30553)83765
38.8%
(Missing)123390
57.1%
ValueCountFrequency (%)
1.88723182718
 
< 0.1%
1.9812948734
< 0.1%
2.09632134411
 
< 0.1%
2.1360504631
 
< 0.1%
2.1663966182
 
< 0.1%
2.21134781814
 
< 0.1%
2.2134282596
 
< 0.1%
2.29080605549
< 0.1%
2.2943544391
 
< 0.1%
2.2969858652
 
< 0.1%
ValueCountFrequency (%)
13.63318927
 
< 0.1%
13.6125698192
< 0.1%
13.592367177
 
< 0.1%
13.572564131
 
< 0.1%
13.554813391
 
< 0.1%
13.553146364
 
< 0.1%
13.534097672
 
< 0.1%
13.52733041
 
< 0.1%
13.5154056510
 
< 0.1%
13.4970569618
 
< 0.1%

neighbours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct153
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.0222984
Minimum1
Maximum7750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:19.398232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q321
95-th percentile1277
Maximum7750
Range7749
Interquartile range (IQR)19

Descriptive statistics

Standard deviation1446.839516
Coefficient of variation (CV)4.39739046
Kurtosis21.8488223
Mean329.0222984
Median Absolute Deviation (MAD)4
Skewness4.844257778
Sum71062236
Variance2093344.585
MonotonicityNot monotonic
2022-01-20T10:36:19.597699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134057
15.8%
428288
 
13.1%
224246
 
11.2%
313152
 
6.1%
510280
 
4.8%
67788
 
3.6%
77507750
 
3.6%
87520
 
3.5%
75691
 
2.6%
94320
 
2.0%
Other values (143)72888
33.7%
ValueCountFrequency (%)
134057
15.8%
224246
11.2%
313152
 
6.1%
428288
13.1%
510280
 
4.8%
67788
 
3.6%
75691
 
2.6%
87520
 
3.5%
94320
 
2.0%
104060
 
1.9%
ValueCountFrequency (%)
77507750
3.6%
18521852
 
0.9%
12771277
 
0.6%
869869
 
0.4%
509509
 
0.2%
388388
 
0.2%
359359
 
0.2%
351351
 
0.2%
350350
 
0.2%
343343
 
0.2%

neighbours_cluster
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct147
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.8740809
Minimum0
Maximum7298
Zeros28054
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:19.802155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q316
95-th percentile865
Maximum7298
Range7298
Interquartile range (IQR)15

Descriptive statistics

Standard deviation1325.854417
Coefficient of variation (CV)4.511641219
Kurtosis23.33629975
Mean293.8740809
Median Absolute Deviation (MAD)3
Skewness4.98856903
Sum63470924
Variance1757889.934
MonotonicityNot monotonic
2022-01-20T10:36:20.011557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137189
17.2%
229096
13.5%
028054
13.0%
314550
 
6.7%
411544
 
5.3%
58010
 
3.7%
72987298
 
3.4%
66810
 
3.2%
75117
 
2.4%
84128
 
1.9%
Other values (137)64184
29.7%
ValueCountFrequency (%)
028054
13.0%
137189
17.2%
229096
13.5%
314550
 
6.7%
411544
 
5.3%
58010
 
3.7%
66810
 
3.2%
75117
 
2.4%
84128
 
1.9%
93393
 
1.6%
ValueCountFrequency (%)
72987298
3.4%
18401840
 
0.9%
12391239
 
0.6%
865865
 
0.4%
508508
 
0.2%
388388
 
0.2%
358358
 
0.2%
351351
 
0.2%
342342
 
0.2%
340340
 
0.2%

neighbours_!cluster
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.584739328
Minimum0
Maximum452
Zeros187926
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2022-01-20T10:36:20.209027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum452
Range452
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.19296967
Coefficient of variation (CV)13.37315816
Kurtosis425.6275525
Mean1.584739328
Median Absolute Deviation (MAD)0
Skewness20.35366196
Sum342272
Variance449.1419633
MonotonicityNot monotonic
2022-01-20T10:36:20.396526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0187926
87.0%
212810
 
5.9%
15202
 
2.4%
42864
 
1.3%
61614
 
0.7%
31068
 
0.5%
8616
 
0.3%
5460
 
0.2%
452452
 
0.2%
10450
 
0.2%
Other values (21)2518
 
1.2%
ValueCountFrequency (%)
0187926
87.0%
15202
 
2.4%
212810
 
5.9%
31068
 
0.5%
42864
 
1.3%
5460
 
0.2%
61614
 
0.7%
7224
 
0.1%
8616
 
0.3%
9144
 
0.1%
ValueCountFrequency (%)
452452
0.2%
146146
 
0.1%
8888
 
< 0.1%
7676
 
< 0.1%
3876
 
< 0.1%
3264
 
< 0.1%
2678
 
< 0.1%
2525
 
< 0.1%
2424
 
< 0.1%
2323
 
< 0.1%

cluster_dst
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size13.8 MiB
C7
133842 
C2
23053 
C0
19854 
C5
 
9135
C6
 
8632
Other values (5)
21464 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC2
2nd rowC7
3rd rowC2
4th rowC7
5th rowC7

Common Values

ValueCountFrequency (%)
C7133842
62.0%
C223053
 
10.7%
C019854
 
9.2%
C59135
 
4.2%
C68632
 
4.0%
C37865
 
3.6%
C97313
 
3.4%
C84653
 
2.2%
C41614
 
0.7%
C119
 
< 0.1%

Length

2022-01-20T10:36:20.571866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-20T10:36:20.669640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
c7133842
62.0%
c223053
 
10.7%
c019854
 
9.2%
c59135
 
4.2%
c68632
 
4.0%
c37865
 
3.6%
c97313
 
3.4%
c84653
 
2.2%
c41614
 
0.7%
c119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cluster_equal
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
True
187926 
False
28054 
ValueCountFrequency (%)
True187926
87.0%
False28054
 
13.0%
2022-01-20T10:36:20.985758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2022-01-20T10:36:10.127548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:46.701156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:49.313124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:52.021930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:54.467388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:57.147838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:59.520376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:02.254859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:04.610606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:07.214988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:10.415777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:46.990381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:49.589385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:52.284189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:54.749471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:57.414569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:59.736836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:02.481292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:04.891892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:07.508206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:10.712981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:47.258666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:49.878572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:52.574414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:55.040688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:57.692985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:00.319772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:02.765644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:05.201025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:07.852305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:11.007230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:47.530898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:50.142867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:52.820797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:55.323143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:57.912710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:00.607710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:02.986729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:05.510233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:08.151831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:11.229640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:47.749313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:50.379273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:53.065273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:55.566740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:58.174723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:00.853860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:03.211169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:05.730826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:08.591693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:11.458028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:47.962700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:50.593694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:53.247786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:55.773152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:58.437901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:01.095068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:03.433535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:05.931290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:08.778156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:11.665473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:48.174169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:50.812163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:53.464036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:55.971623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:58.648792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:01.325546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:03.651949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:06.140733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:08.986597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:11.916760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:48.527187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:51.061460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:53.711373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:56.252125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:58.840317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:01.548750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:03.870430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:06.415957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:09.267883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:12.167128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:48.798462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:51.346697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:53.953763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:56.547589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:59.088569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:01.781127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:04.108755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:06.692258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:09.583038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:12.482306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:49.068740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:51.773597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:54.225998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:56.877559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:35:59.297016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:02.035449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:04.320154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:06.958535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-01-20T10:36:09.872265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-01-20T10:36:21.078554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-20T10:36:21.344799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-20T10:36:21.605141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-20T10:36:21.861416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-20T10:36:22.067648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-20T10:36:12.959030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-20T10:36:13.646230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-20T10:36:14.551401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-20T10:36:14.805735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

srcdstdatecluster_srcmes_sentmes_receivedmes_totalcontr_indexsentiment_avgemoti_avgcompl_avgneighboursneighbours_clusterneighbours_!clustercluster_dstcluster_equal
0000126373boh73hh2021-12-20 00:00:00C21011.0NaNNaNNaN420C2True
1000126373boh73hh2021-12-20 00:00:00C21011.0NaNNaNNaN402C7False
2000126373boh73hh2021-12-20 00:00:00C71011.00.1593750.2938724.385481402C2False
3000126373boh73hh2021-12-20 00:00:00C71011.00.1593750.2938724.385481420C7True
40001_yousefdogelon_muskk2021-12-20 00:00:00C71011.0NaNNaNNaN11100C7True
50001_yousef_cryptoshepherd2021-12-20 00:00:00C71011.00.0610160.2623934.57282511100C7True
60001_yousefwassieworld2021-12-26 00:00:00C71011.00.6456560.1488966.26455111100C7True
70001_yousefaaveaave2021-12-26 00:00:00C71011.00.6456560.1488966.2645511101C2False
80001_yousefaaveaave2021-12-26 00:00:00C71011.00.6456560.1488966.26455111100C7True
90001_yousefcryptocomarena2021-12-26 00:00:00C71011.00.6456560.1488966.26455111100C7True

Last rows

srcdstdatecluster_srcmes_sentmes_receivedmes_totalcontr_indexsentiment_avgemoti_avgcompl_avgneighboursneighbours_clusterneighbours_!clustercluster_dstcluster_equal
215970zzlicartheshibking2021-11-29 00:00:00C71011.00.6129910.4081435.040456110C7True
215971zzsami69asraful787842021-11-07 00:00:00C0011-1.0NaNNaNNaN110C0True
215972zzz_aliaaaashwsbreal2021-12-20 00:00:00C71011.00.4872860.4190825.956730110C7True
215973zzzaaagggaaailomehn2021-12-05 00:00:00C5011-1.0NaNNaNNaN110C5True
215974zzzaferrrellocobielsaa2021-11-29 00:00:00C0011-1.0NaNNaNNaN210C0True
215975zzzaferrrellocobielsaa2021-11-29 00:00:00C0011-1.0NaNNaNNaN201C7False
215976zzznejipeitosdotorao2021-12-12 00:00:00C3011-1.0NaNNaNNaN420C3True
215977zzznejipeitosdotorao2021-12-12 00:00:00C3011-1.0NaNNaNNaN402C9False
215978zzznejipeitosdotorao2021-12-12 00:00:00C9011-1.0NaNNaNNaN402C3False
215979zzznejipeitosdotorao2021-12-12 00:00:00C9011-1.0NaNNaNNaN420C9True